it’s spot s a ny wea k ness, it w i l l focu s on
that and thereby Force the learner to learn to not
have that weakness anymore, like one
form of adversarial training people. Sometimes do
is if you have a game playing
You m a k e it p l a y it s e l f a l ot of t i m e s b e c a u s e
Full-time, they are trying to look for weaknesses
in their opponent and exploit those
wea k nesses. A nd when t hey do t hat, t hey’re
forced to then improve or fix those weaknesses in
themselves because their opponent is
exploiting those weaknesses. So Every time the
every time the system finds a strategy
that is extremely good against this opponent the
t he opponent w ho’s a lso t hem ha s to lea r n
a way of dealing with that strategy and so on and
so on so as the system gets better it forces
it sel f to g et bet ter beca u se it’s cont i nuou sly
having to learn how to play a better and
bet ter opponent . It’s qu ite it’s qu ite eleg a nt , you
k now, t his is where we get to generative
adversarial networks.
L et’s say you’ve g ot a net work you w a nt to
L et’s say you w a nt ca t pict u res.
You k n ow you w a nt t o b e a b l e t o g i v e it a b u n c h
of pictures of cats and have it spit out
a new picture of a cat that you’ve never seen
before that looks exactly like a cat the
way t hey’re degenerative adversa ria l net work
work s is it’s t h is a rch itect u re w here
you actually have two networks. One of the
Net work ’s is t he d isc r i m i na tor w ho ha s
l e s s s p e l l i n g. Ye a h . I l i k e t h a t . T h e d i s c r i m i n a t or
net work is a classif ier, right? So
straightforward classifier you give it an image
and it outputs a number between 0 and 1 and
your training that in standard supervised
lea r n i ng way. T hen you have a A nd t he generator
Is usually a convolutional neural network.
A lt hou gh act ua l ly, bot h of t hese ca n be ot her